import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import cached_download, hf_hub_url
from PIL import Image

from transformers import DPTConfig, DPTForDepthEstimation, DPTForSemanticSegmentation, DPTImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
lowerCamelCase__  =					logging.get_logger(__name__)


def 				lowerCAmelCase__       (			a__ )      ->Dict:




  '''simple docstring'''

  _UpperCamelCase           =    DPTConfig(embedding_type="hybrid" )

  if "large" in checkpoint_url:
    _UpperCamelCase           =    1_024
    _UpperCamelCase           =    4_096
    _UpperCamelCase           =    24
    _UpperCamelCase           =    16
    _UpperCamelCase           =    [5, 11, 17, 23]
    _UpperCamelCase           =    [256, 512, 1_024, 1_024]
    _UpperCamelCase           =    (1, 384, 384)

  if "nyu" or "midas" in checkpoint_url:
    _UpperCamelCase           =    768
    _UpperCamelCase           =    [1, 1, 1, 0.5]
    _UpperCamelCase           =    [256, 512, 768, 768]
    _UpperCamelCase           =    150
    _UpperCamelCase           =    16
    _UpperCamelCase           =    (1, 384, 384)
    _UpperCamelCase           =    False
    _UpperCamelCase           =    "project"

  if "ade" in checkpoint_url:
    _UpperCamelCase           =    True
    _UpperCamelCase           =    768
    _UpperCamelCase           =    [1, 1, 1, 0.5]
    _UpperCamelCase           =    150
    _UpperCamelCase           =    16
    _UpperCamelCase           =    "huggingface/label-files"
    _UpperCamelCase           =    "ade20k-id2label.json"
    _UpperCamelCase           =    json.load(open(cached_download(hf_hub_url(a__    ,     a__    ,     repo_type="dataset" ) )    ,     "r" ) )
    _UpperCamelCase           =    {int(a__ ): v for k, v in idalabel.items()}
    _UpperCamelCase           =    idalabel
    _UpperCamelCase           =    {v: k for k, v in idalabel.items()}
    _UpperCamelCase           =    [1, 150, 480, 480]

  return config, expected_shape


def 				lowerCAmelCase__       (			a__ )      ->int:




  '''simple docstring'''

  _UpperCamelCase           =    ["pretrained.model.head.weight", "pretrained.model.head.bias"]
  for k in ignore_keys:
    state_dict.pop(a__    ,     a__ )


def 				lowerCAmelCase__       (			a__ )      ->Dict:




  '''simple docstring'''

  if (
      "pretrained.model" in name
      and "cls_token" not in name
      and "pos_embed" not in name
      and "patch_embed" not in name
  ):
    _UpperCamelCase           =    name.replace("pretrained.model"    ,     "dpt.encoder" )
  if "pretrained.model" in name:
    _UpperCamelCase           =    name.replace("pretrained.model"    ,     "dpt.embeddings" )
  if "patch_embed" in name:
    _UpperCamelCase           =    name.replace("patch_embed"    ,     "" )
  if "pos_embed" in name:
    _UpperCamelCase           =    name.replace("pos_embed"    ,     "position_embeddings" )
  if "attn.proj" in name:
    _UpperCamelCase           =    name.replace("attn.proj"    ,     "attention.output.dense" )
  if "proj" in name and "project" not in name:
    _UpperCamelCase           =    name.replace("proj"    ,     "projection" )
  if "blocks" in name:
    _UpperCamelCase           =    name.replace("blocks"    ,     "layer" )
  if "mlp.fc1" in name:
    _UpperCamelCase           =    name.replace("mlp.fc1"    ,     "intermediate.dense" )
  if "mlp.fc2" in name:
    _UpperCamelCase           =    name.replace("mlp.fc2"    ,     "output.dense" )
  if "norm1" in name and "backbone" not in name:
    _UpperCamelCase           =    name.replace("norm1"    ,     "layernorm_before" )
  if "norm2" in name and "backbone" not in name:
    _UpperCamelCase           =    name.replace("norm2"    ,     "layernorm_after" )
  if "scratch.output_conv" in name:
    _UpperCamelCase           =    name.replace("scratch.output_conv"    ,     "head" )
  if "scratch" in name:
    _UpperCamelCase           =    name.replace("scratch"    ,     "neck" )
  if "layer1_rn" in name:
    _UpperCamelCase           =    name.replace("layer1_rn"    ,     "convs.0" )
  if "layer2_rn" in name:
    _UpperCamelCase           =    name.replace("layer2_rn"    ,     "convs.1" )
  if "layer3_rn" in name:
    _UpperCamelCase           =    name.replace("layer3_rn"    ,     "convs.2" )
  if "layer4_rn" in name:
    _UpperCamelCase           =    name.replace("layer4_rn"    ,     "convs.3" )
  if "refinenet" in name:
    _UpperCamelCase           =    int(name[len("neck.refinenet" ) : len("neck.refinenet" ) + 1] )
    # tricky here: we need to map 4 to 0, 3 to 1, 2 to 2 and 1 to 3
    _UpperCamelCase           =    name.replace(f'refinenet{layer_idx}'    ,     f'fusion_stage.layers.{abs(layer_idx-4 )}' )
  if "out_conv" in name:
    _UpperCamelCase           =    name.replace("out_conv"    ,     "projection" )
  if "resConfUnit1" in name:
    _UpperCamelCase           =    name.replace("resConfUnit1"    ,     "residual_layer1" )
  if "resConfUnit2" in name:
    _UpperCamelCase           =    name.replace("resConfUnit2"    ,     "residual_layer2" )
  if "conv1" in name:
    _UpperCamelCase           =    name.replace("conv1"    ,     "convolution1" )
  if "conv2" in name:
    _UpperCamelCase           =    name.replace("conv2"    ,     "convolution2" )
  # readout blocks
  if "pretrained.act_postprocess1.0.project.0" in name:
    _UpperCamelCase           =    name.replace("pretrained.act_postprocess1.0.project.0"    ,     "neck.reassemble_stage.readout_projects.0.0" )
  if "pretrained.act_postprocess2.0.project.0" in name:
    _UpperCamelCase           =    name.replace("pretrained.act_postprocess2.0.project.0"    ,     "neck.reassemble_stage.readout_projects.1.0" )
  if "pretrained.act_postprocess3.0.project.0" in name:
    _UpperCamelCase           =    name.replace("pretrained.act_postprocess3.0.project.0"    ,     "neck.reassemble_stage.readout_projects.2.0" )
  if "pretrained.act_postprocess4.0.project.0" in name:
    _UpperCamelCase           =    name.replace("pretrained.act_postprocess4.0.project.0"    ,     "neck.reassemble_stage.readout_projects.3.0" )

  # resize blocks
  if "pretrained.act_postprocess1.3" in name:
    _UpperCamelCase           =    name.replace("pretrained.act_postprocess1.3"    ,     "neck.reassemble_stage.layers.0.projection" )
  if "pretrained.act_postprocess1.4" in name:
    _UpperCamelCase           =    name.replace("pretrained.act_postprocess1.4"    ,     "neck.reassemble_stage.layers.0.resize" )
  if "pretrained.act_postprocess2.3" in name:
    _UpperCamelCase           =    name.replace("pretrained.act_postprocess2.3"    ,     "neck.reassemble_stage.layers.1.projection" )
  if "pretrained.act_postprocess2.4" in name:
    _UpperCamelCase           =    name.replace("pretrained.act_postprocess2.4"    ,     "neck.reassemble_stage.layers.1.resize" )
  if "pretrained.act_postprocess3.3" in name:
    _UpperCamelCase           =    name.replace("pretrained.act_postprocess3.3"    ,     "neck.reassemble_stage.layers.2.projection" )
  if "pretrained.act_postprocess4.3" in name:
    _UpperCamelCase           =    name.replace("pretrained.act_postprocess4.3"    ,     "neck.reassemble_stage.layers.3.projection" )
  if "pretrained.act_postprocess4.4" in name:
    _UpperCamelCase           =    name.replace("pretrained.act_postprocess4.4"    ,     "neck.reassemble_stage.layers.3.resize" )
  if "pretrained" in name:
    _UpperCamelCase           =    name.replace("pretrained"    ,     "dpt" )
  if "bn" in name:
    _UpperCamelCase           =    name.replace("bn"    ,     "batch_norm" )
  if "head" in name:
    _UpperCamelCase           =    name.replace("head"    ,     "head.head" )
  if "encoder.norm" in name:
    _UpperCamelCase           =    name.replace("encoder.norm"    ,     "layernorm" )
  if "auxlayer" in name:
    _UpperCamelCase           =    name.replace("auxlayer"    ,     "auxiliary_head.head" )
  if "backbone" in name:
    _UpperCamelCase           =    name.replace("backbone"    ,     "backbone.bit.encoder" )

  if ".." in name:
    _UpperCamelCase           =    name.replace(".."    ,     "." )

  if "stem.conv" in name:
    _UpperCamelCase           =    name.replace("stem.conv"    ,     "bit.embedder.convolution" )
  if "blocks" in name:
    _UpperCamelCase           =    name.replace("blocks"    ,     "layers" )
  if "convolution" in name and "backbone" in name:
    _UpperCamelCase           =    name.replace("convolution"    ,     "conv" )
  if "layer" in name and "backbone" in name:
    _UpperCamelCase           =    name.replace("layer"    ,     "layers" )
  if "backbone.bit.encoder.bit" in name:
    _UpperCamelCase           =    name.replace("backbone.bit.encoder.bit"    ,     "backbone.bit" )
  if "embedder.conv" in name:
    _UpperCamelCase           =    name.replace("embedder.conv"    ,     "embedder.convolution" )
  if "backbone.bit.encoder.stem.norm" in name:
    _UpperCamelCase           =    name.replace("backbone.bit.encoder.stem.norm"    ,     "backbone.bit.embedder.norm" )
  return name


def 				lowerCAmelCase__       (			a__    ,     a__ )      ->Optional[Any]:




  '''simple docstring'''

  for i in range(config.num_hidden_layers ):
    # read in weights + bias of input projection layer (in timm, this is a single matrix + bias)
    _UpperCamelCase           =    state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.weight' )
    _UpperCamelCase           =    state_dict.pop(f'dpt.encoder.layer.{i}.attn.qkv.bias' )
    # next, add query, keys and values (in that order) to the state dict
    _UpperCamelCase           =    in_proj_weight[: config.hidden_size, :]
    _UpperCamelCase           =    in_proj_bias[: config.hidden_size]
    _UpperCamelCase           =    in_proj_weight[
        config.hidden_size : config.hidden_size * 2, :
    ]
    _UpperCamelCase           =    in_proj_bias[
        config.hidden_size : config.hidden_size * 2
    ]
    _UpperCamelCase           =    in_proj_weight[
        -config.hidden_size :, :
    ]
    _UpperCamelCase           =    in_proj_bias[-config.hidden_size :]


def 				lowerCAmelCase__       (			)      ->Optional[Any]:




  '''simple docstring'''

  _UpperCamelCase           =    "http://images.cocodataset.org/val2017/000000039769.jpg"
  _UpperCamelCase           =    Image.open(requests.get(a__    ,     stream=a__ ).raw )
  return im


@torch.no_grad()
def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__    ,     a__ )      ->Optional[Any]:




  '''simple docstring'''

  _UpperCamelCase      ,						_UpperCamelCase           =    get_dpt_config(a__ )
  # load original state_dict from URL
  # state_dict = torch.hub.load_state_dict_from_url(checkpoint_url, map_location="cpu")
  _UpperCamelCase           =    torch.load(a__    ,     map_location="cpu" )
  # remove certain keys
  remove_ignore_keys_(a__ )
  # rename keys
  for key in state_dict.copy().keys():
    _UpperCamelCase           =    state_dict.pop(a__ )
    _UpperCamelCase           =    val
  # read in qkv matrices
  read_in_q_k_v(a__    ,     a__ )

  # load HuggingFace model
  _UpperCamelCase           =    DPTForSemanticSegmentation(a__ ) if "ade" in checkpoint_url else DPTForDepthEstimation(a__ )
  model.load_state_dict(a__ )
  model.eval()

  # Check outputs on an image
  _UpperCamelCase           =    480 if "ade" in checkpoint_url else 384
  _UpperCamelCase           =    DPTImageProcessor(size=a__ )

  _UpperCamelCase           =    prepare_img()
  _UpperCamelCase           =    image_processor(a__    ,     return_tensors="pt" )

  # forward pass
  _UpperCamelCase           =    model(**a__ ).logits if "ade" in checkpoint_url else model(**a__ ).predicted_depth

  if show_prediction:
    _UpperCamelCase           =    (
        torch.nn.functional.interpolate(
            outputs.unsqueeze(1 )    ,     size=(image.size[1], image.size[0])    ,     mode="bicubic"    ,     align_corners=a__    ,     )
        .squeeze()
        .cpu()
        .numpy()
    )

    Image.fromarray((prediction / prediction.max()) * 255 ).show()

  if pytorch_dump_folder_path is not None:
    Path(a__ ).mkdir(exist_ok=a__ )
    print(f'Saving model to {pytorch_dump_folder_path}' )
    model.save_pretrained(a__ )
    print(f'Saving image processor to {pytorch_dump_folder_path}' )
    image_processor.save_pretrained(a__ )

  if push_to_hub:
    model.push_to_hub("ybelkada/dpt-hybrid-midas" )
    image_processor.push_to_hub("ybelkada/dpt-hybrid-midas" )


if __name__ == "__main__":
   lowerCamelCase__  =					argparse.ArgumentParser()
   # Required parameters
   parser.add_argument(
       '''--checkpoint_url''',
       default='''https://github.com/intel-isl/DPT/releases/download/1_0/dpt_large-midas-2f21e586.pt''',
       type=str,
       help='''URL of the original DPT checkpoint you\'d like to convert.''',
   )
   parser.add_argument(
       '''--pytorch_dump_folder_path''',
       default=None,
       type=str,
       required=False,
       help='''Path to the output PyTorch model directory.''',
   )
   parser.add_argument(
       '''--push_to_hub''',
       action='''store_true''',
   )
   parser.add_argument(
       '''--model_name''',
       default='''dpt-large''',
       type=str,
       help='''Name of the model, in case you\'re pushing to the hub.''',
   )
   parser.add_argument(
       '''--show_prediction''',
       action='''store_true''',
   )

   lowerCamelCase__  =					parser.parse_args()
   convert_dpt_checkpoint(
       args.checkpoint_url, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name, args.show_prediction
   )





# This code is adapted from OpenAI's release
# https://github.com/openai/human-eval/blob/master/human_eval/execution.py

import contextlib
import faulthandler
import io
import multiprocessing
import os
import platform
import signal
import tempfile


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__    ,     a__ )      ->str:




  '''simple docstring'''

  _UpperCamelCase           =    multiprocessing.Manager()
  _UpperCamelCase           =    manager.list()

  _UpperCamelCase           =    multiprocessing.Process(target=a__    ,     args=(check_program, result, timeout) )
  p.start()
  p.join(timeout=timeout + 1 )
  if p.is_alive():
    p.kill()

  if not result:
    result.append("timed out" )

  return {
      "task_id": task_id,
      "passed": result[0] == "passed",
      "result": result[0],
      "completion_id": completion_id,
  }


def 				lowerCAmelCase__       (			a__    ,     a__    ,     a__ )      ->Union[str, Any]:




  '''simple docstring'''

  with create_tempdir():
    # These system calls are needed when cleaning up tempdir.
    import os
    import shutil

    _UpperCamelCase           =    shutil.rmtree
    _UpperCamelCase           =    os.rmdir
    _UpperCamelCase           =    os.chdir

    # Disable functionalities that can make destructive changes to the test.
    reliability_guard()

    # Run program.
    try:
      _UpperCamelCase           =    {}
      with swallow_io():
        with time_limit(a__ ):
          exec(a__    ,     a__ )
      result.append("passed" )
    except TimeoutException:
      result.append("timed out" )
    except BaseException as e:
      result.append(f'failed: {e}' )

    # Needed for cleaning up.
    _UpperCamelCase           =    rmtree
    _UpperCamelCase           =    rmdir
    _UpperCamelCase           =    chdir


@contextlib.contextmanager
def 				lowerCAmelCase__       (			a__ )      ->Optional[Any]:




  '''simple docstring'''

  def signal_handler(a__    ,     a__ ):
    raise TimeoutException("Timed out!" )

  signal.setitimer(signal.ITIMER_REAL    ,     a__ )
  signal.signal(signal.SIGALRM    ,     a__ )
  try:
    yield
  finally:
    signal.setitimer(signal.ITIMER_REAL    ,     0 )


@contextlib.contextmanager
def 				lowerCAmelCase__       (			)      ->Optional[int]:




  '''simple docstring'''

  _UpperCamelCase           =    WriteOnlyStringIO()
  with contextlib.redirect_stdout(a__ ):
    with contextlib.redirect_stderr(a__ ):
      with redirect_stdin(a__ ):
        yield


@contextlib.contextmanager
def 				lowerCAmelCase__       (			)      ->Optional[int]:




  '''simple docstring'''

  with tempfile.TemporaryDirectory() as dirname:
    with chdir(a__ ):
      yield dirname




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 pass




class 					_UpperCAmelCase			(     io.StringIO  ):



 '''simple docstring'''




 def 		__UpperCAmelCase				(		self				:   List[str]							, *lowercase_				:   str							, **lowercase_				:   List[str])						->	Optional[Any]:


   """simple docstring"""







   raise OSError







 def 		__UpperCAmelCase				(		self				:   Any							, *lowercase_				:   List[Any]							, **lowercase_				:   Optional[int])						->	Optional[int]:


   """simple docstring"""







   raise OSError







 def 		__UpperCAmelCase				(		self				:   int							, *lowercase_				:   int							, **lowercase_				:   str)						->	int:


   """simple docstring"""







   raise OSError







 def 		__UpperCAmelCase				(		self				:   List[Any]							, *lowercase_				:   Tuple							, **lowercase_				:   Tuple)						->	Tuple:


   """simple docstring"""







   return False




class 					_UpperCAmelCase			(     contextlib._RedirectStream  ):  # type: ignore



 '''simple docstring'''




 __A			       =					'''stdin'''


@contextlib.contextmanager
def 				lowerCAmelCase__       (			a__ )      ->str:




  '''simple docstring'''

  if root == ".":
    yield
    return
  _UpperCamelCase           =    os.getcwd()
  os.chdir(a__ )
  try:
    yield
  except BaseException as exc:
    raise exc
  finally:
    os.chdir(a__ )







def 				lowerCAmelCase__       (			a__=None )      ->Optional[int]:




  '''simple docstring'''

  if maximum_memory_bytes is not None:
    import resource

    resource.setrlimit(resource.RLIMIT_AS    ,     (maximum_memory_bytes, maximum_memory_bytes) )
    resource.setrlimit(resource.RLIMIT_DATA    ,     (maximum_memory_bytes, maximum_memory_bytes) )
    if not platform.uname().system == "Darwin":
      resource.setrlimit(resource.RLIMIT_STACK    ,     (maximum_memory_bytes, maximum_memory_bytes) )

  faulthandler.disable()

  import builtins

  _UpperCamelCase           =    None
  _UpperCamelCase           =    None

  import os

  _UpperCamelCase           =    "1"

  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None

  import shutil

  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None

  import subprocess

  _UpperCamelCase           =    None  # type: ignore

  _UpperCamelCase           =    None

  import sys

  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None
  _UpperCamelCase           =    None




